TechniqueRLHF / Alignment4 recent entries11 Apr 2026All ToolsAuto-generated index of all tools mentioned across the wiki.→11 Apr 2026All CompaniesAuto-generated index of all companies mentioned across the wiki.→13 Jul 2026Securing the AI supply chain on GKE: Introducing k8s-aibom for automated AI BOMsHow should your security team manage shadow AI? Workloads deployed by developers without formal registration can often evade traditional security scanners, because organizations are reluctant to slow
TechniqueRAG8 recent entries13 Jul 2026Securing the AI supply chain on GKE: Introducing k8s-aibom for automated AI BOMsHow should your security team manage shadow AI? Workloads deployed by developers without formal registration can often evade traditional security scanners, because organizations are reluctant to slow →7 Aug 2026100% Local RAG Without Internet and Without OllamaBuild a 100% offline fast Retrieval Augmented Generation (RAG) system that runs without an internet connection, without cloud APIs, without OpenAI/Ollama Published a video where you can build a fully →13 Aug 2026Qdrant and Minima Deliver 2.92x More Agentic RAG Tasks per GPU-HourReducing Retrieval and Calls When a retrieval-augmented generation (RAG) agent runs, it often has to plan a search, check the evidence it gets back, and try again when that evidence falls short. Those→3 Sept 2026Hybrid Retrieval-Augmented Generation with Knowledge Graph Expansion, RRF Fusion, and Per-Chunk Grounded Evaluation for Enterprise Document SearcharXiv:2609.01617v1 Announce Type: cross Abstract: Getting accurate, grounded answers out of large enterprise document repositories is a difficult problem. Dense vector retrieval alone frequently perfo→8 Sept 2026My lab found a way to migrate between embedding models with zero downtime. [R]So I've been messinga round with embedding models for a bit, and I think they are interesting enough to experiment with. They are useful for rag, especially in a localllm sense because you can ground →8 Sept 2026An open-source context layer for building AI on top of company dataWe’ve been building PipesHub for a while now, and I’d love to get more teams to try it and tell us where it breaks. The problem we kept running into was pretty simple: Building an AI app over company →16 Sept 2026M4N VM family, now GA: Highest per-core IOPS and throughput for I/O and memory-bound workloadsAs enterprise organizations scale mission-critical applications, storage I/O and memory access can become severe operational bottlenecks. Whether its Oracle databases, in-memory databases like SAP HAN→29 Sept 2026Constella Preview: Swap Query Models Without Re-EmbeddingAs query traffic grows, so does the compute bill for embedding it. On a low-power device, a large model may not fit in memory. A smaller query model could reduce that cost, but switching usually means
TechniqueAgents8 recent entries24 Jul 2026Optimizing an Ollama (Qwen:2.5) AI Agent: Fixing Search Aggregation, Context Bleed, and Query ExtractionI am building a domain-specific AI agent powered by Ollama (using the qwen:2.5 model). For data retrieval, the agent utilizes multiple search APIs: DuckDuckGo Search (DDGS), Tavily, Serper, and Google→10 Aug 2026How to Clean Up a Qdrant CollectionEvery crawl, retried job, and embedding pipeline change writes points into a vector collection. The stored data keeps moving even when the query code never changes, and the top results move with it. A→13 Aug 2026Qdrant and Minima Deliver 2.92x More Agentic RAG Tasks per GPU-HourReducing Retrieval and Calls When a retrieval-augmented generation (RAG) agent runs, it often has to plan a search, check the evidence it gets back, and try again when that evidence falls short. Those→3 Sept 2026Hybrid Retrieval-Augmented Generation with Knowledge Graph Expansion, RRF Fusion, and Per-Chunk Grounded Evaluation for Enterprise Document SearcharXiv:2609.01617v1 Announce Type: cross Abstract: Getting accurate, grounded answers out of large enterprise document repositories is a difficult problem. Dense vector retrieval alone frequently perfo→7 Sept 2026BIT.UA at BioASQ 14B: Modular Retrieval with pg_textsearch and Qdrant, and Agent-Based Answer GenerationarXiv:2609.04999v1 Announce Type: new Abstract: This paper describes the participation of the BIT.UA team from the University of Aveiro in the 14th edition of the BioASQ Task B challenge on biomedical→8 Sept 2026An open-source context layer for building AI on top of company dataWe’ve been building PipesHub for a while now, and I’d love to get more teams to try it and tell us where it breaks. The problem we kept running into was pretty simple: Building an AI app over company →30 Sept 2026Vulnerability Discovery and Exploitation Trends in the AI EraWritten by: Robin Grunewald, Supriya Mazumdar, Kelli Vanderlee Introduction Google Threat Intelligence Group (GTIG) examines vulnerability disclosure and exploitation statistics to evaluate the impact→5 Oct 2026ZeBROD: Zero-Retraining Based Recognition and Object Detection FrameworkarXiv:2512.04888v4 Announce Type: replace Abstract: Object detection constitutes the primary task within the domain of computer vision. It is utilized in numerous domains. Nonetheless, object detectio
TechniqueFine-tuning3 recent entries11 Apr 2026All ToolsAuto-generated index of all tools mentioned across the wiki.→11 Apr 2026All CompaniesAuto-generated index of all companies mentioned across the wiki.→16 Apr 2026SemiFA: An Agentic Multi-Modal Framework for Autonomous Semiconductor Failure Analysis Report GenerationarXiv:2604.13236v1 Announce Type: new Abstract: Semiconductor failure analysis (FA) requires engineers to examine inspection images, correlate equipment telemetry, consult historical defect records, a
TechniqueMultimodal5 recent entries11 Apr 2026All ToolsAuto-generated index of all tools mentioned across the wiki.→11 Apr 2026All CompaniesAuto-generated index of all companies mentioned across the wiki.→16 Apr 2026SemiFA: An Agentic Multi-Modal Framework for Autonomous Semiconductor Failure Analysis Report GenerationarXiv:2604.13236v1 Announce Type: new Abstract: Semiconductor failure analysis (FA) requires engineers to examine inspection images, correlate equipment telemetry, consult historical defect records, a→5 Oct 2026ZeBROD: Zero-Retraining Based Recognition and Object Detection FrameworkarXiv:2512.04888v4 Announce Type: replace Abstract: Object detection constitutes the primary task within the domain of computer vision. It is utilized in numerous domains. Nonetheless, object detectio→8 Oct 2026Jevjitsu: Or, How We Tried Generalized Classifiers on EverythingClassification is one of the oldest problems in machine learning. Unlike modern decoding transformers, most classification models were non-autoregressive by nature. Before Jev, the common approaches w
TechniqueSafety4 recent entries11 Apr 2026All ToolsAuto-generated index of all tools mentioned across the wiki.→11 Apr 2026All CompaniesAuto-generated index of all companies mentioned across the wiki.→30 Sept 2026Vulnerability Discovery and Exploitation Trends in the AI EraWritten by: Robin Grunewald, Supriya Mazumdar, Kelli Vanderlee Introduction Google Threat Intelligence Group (GTIG) examines vulnerability disclosure and exploitation statistics to evaluate the impact→8 Oct 2026Jevjitsu: Or, How We Tried Generalized Classifiers on EverythingClassification is one of the oldest problems in machine learning. Unlike modern decoding transformers, most classification models were non-autoregressive by nature. Before Jev, the common approaches w